# training with captions

# Swap blocks between CPU and GPU:
# This implementation is inspired by and based on the work of 2kpr.
# Many thanks to 2kpr for the original concept and implementation of memory-efficient offloading.
# The original idea has been adapted and extended to fit the current project's needs.

# Key features:
# - CPU offloading during forward and backward passes
# - Use of fused optimizer and grad_hook for efficient gradient processing
# - Per-block fused optimizer instances

import argparse
import copy
import math
import os
from multiprocessing import Value
import toml

from tqdm import tqdm

import torch
from .library.device_utils import init_ipex, clean_memory_on_device

init_ipex()

from accelerate.utils import set_seed
from .library import deepspeed_utils, flux_train_utils, flux_utils, strategy_base, strategy_flux
from .library.sd3_train_utils import FlowMatchEulerDiscreteScheduler

from .library import train_util as train_util

from .library.utils import setup_logging, add_logging_arguments

setup_logging()
import logging

logger = logging.getLogger(__name__)

from .library import config_util as config_util

from .library.config_util import (
    ConfigSanitizer,
    BlueprintGenerator,
)
from .library.custom_train_functions import apply_masked_loss, add_custom_train_arguments


class FluxTrainer:
    def __init__(self):
        self.sample_prompts_te_outputs = None
    
    def sample_images(self, epoch, global_step, validation_settings):
        image_tensors = flux_train_utils.sample_images(
        self.accelerator, self.args, epoch, global_step, self.unet, self.vae, self.text_encoder, self.sample_prompts_te_outputs, validation_settings)
        return image_tensors
    
    def init_train(self, args):
        train_util.verify_training_args(args)
        train_util.prepare_dataset_args(args, True)
        # sdxl_train_util.verify_sdxl_training_args(args)
        deepspeed_utils.prepare_deepspeed_args(args)
        setup_logging(args, reset=True)

        # temporary: backward compatibility for deprecated options. remove in the future
        if not args.skip_cache_check:
            args.skip_cache_check = args.skip_latents_validity_check

        if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs:
            logger.warning(
                "cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります"
            )
            args.cache_text_encoder_outputs = True

        if args.cpu_offload_checkpointing and not args.gradient_checkpointing:
            logger.warning(
                "cpu_offload_checkpointing is enabled, so gradient_checkpointing is also enabled / cpu_offload_checkpointingが有効になっているため、gradient_checkpointingも有効になります"
            )
            args.gradient_checkpointing = True

        assert (
            args.blocks_to_swap is None or args.blocks_to_swap == 0
        ) or not args.cpu_offload_checkpointing, (
            "blocks_to_swap is not supported with cpu_offload_checkpointing / blocks_to_swapはcpu_offload_checkpointingと併用できません"
        )

        cache_latents = args.cache_latents
        use_dreambooth_method = args.in_json is None

        if args.seed is not None:
            set_seed(args.seed)

        # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization.
        if args.cache_latents:
            latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy(
                args.cache_latents_to_disk, args.vae_batch_size, args.skip_latents_validity_check
            )
            strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy)

        # Prepare the dataset
        if args.dataset_class is None:
            blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True))
            if args.dataset_config is not None:
                logger.info(f"Load dataset config from {args.dataset_config}")
                user_config = config_util.load_user_config(args.dataset_config)
                ignored = ["train_data_dir", "in_json"]
                if any(getattr(args, attr) is not None for attr in ignored):
                    logger.warning(
                        "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
                            ", ".join(ignored)
                        )
                    )
            else:
                if use_dreambooth_method:
                    logger.info("Using DreamBooth method.")
                    user_config = {
                        "datasets": [
                            {
                                "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(
                                    args.train_data_dir, args.reg_data_dir
                                )
                            }
                        ]
                    }
                else:
                    logger.info("Training with captions.")
                    user_config = {
                        "datasets": [
                            {
                                "subsets": [
                                    {
                                        "image_dir": args.train_data_dir,
                                        "metadata_file": args.in_json,
                                    }
                                ]
                            }
                        ]
                    }

            blueprint = blueprint_generator.generate(user_config, args)
            train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
        else:
            train_dataset_group = train_util.load_arbitrary_dataset(args)

        current_epoch = Value("i", 0)
        current_step = Value("i", 0)
        ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
        collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)

        train_dataset_group.verify_bucket_reso_steps(16)  # TODO これでいいか確認

        _, is_schnell, _, _ = flux_utils.analyze_checkpoint_state(args.pretrained_model_name_or_path)
        if args.debug_dataset:
            if args.cache_text_encoder_outputs:
                strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(
                    strategy_flux.FluxTextEncoderOutputsCachingStrategy(
                        args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, args.skip_cache_check, False
                    )
                )
            t5xxl_max_token_length = (
                args.t5xxl_max_token_length if args.t5xxl_max_token_length is not None else (256 if is_schnell else 512)
            )
            strategy_base.TokenizeStrategy.set_strategy(strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length))

            train_dataset_group.set_current_strategies()
            train_util.debug_dataset(train_dataset_group, True)
            return
        if len(train_dataset_group) == 0:
            logger.error(
                "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。"
            )
            return

        if cache_latents:
            assert (
                train_dataset_group.is_latent_cacheable()
            ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"

        if args.cache_text_encoder_outputs:
            assert (
                train_dataset_group.is_text_encoder_output_cacheable()
            ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"

        # acceleratorを準備する
        logger.info("prepare accelerator")
        accelerator = train_util.prepare_accelerator(args)

        # mixed precisionに対応した型を用意しておき適宜castする
        weight_dtype, save_dtype = train_util.prepare_dtype(args)

        # load VAE for caching latents
        ae = None
        if cache_latents:
            ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu", args.disable_mmap_load_safetensors)
            ae.to(accelerator.device, dtype=weight_dtype)
            ae.requires_grad_(False)
            ae.eval()

            train_dataset_group.new_cache_latents(ae, accelerator)

            ae.to("cpu")  # if no sampling, vae can be deleted
            clean_memory_on_device(accelerator.device)

            accelerator.wait_for_everyone()

        # prepare tokenize strategy
        if args.t5xxl_max_token_length is None:
            if is_schnell:
                t5xxl_max_token_length = 256
            else:
                t5xxl_max_token_length = 512
        else:
            t5xxl_max_token_length = args.t5xxl_max_token_length

        flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length)
        strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy)

        # load clip_l, t5xxl for caching text encoder outputs
        clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu", args.disable_mmap_load_safetensors)
        t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu", args.disable_mmap_load_safetensors)
        clip_l.eval()
        t5xxl.eval()
        clip_l.requires_grad_(False)
        t5xxl.requires_grad_(False)

        text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask)
        strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy)

        # cache text encoder outputs
        sample_prompts_te_outputs = None
        if args.cache_text_encoder_outputs:
            # Text Encodes are eval and no grad here
            clip_l.to(accelerator.device)
            t5xxl.to(accelerator.device)

            text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy(
                args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask
            )
            strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy)

            with accelerator.autocast():
                train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], accelerator)

            # cache sample prompt's embeddings to free text encoder's memory
            if args.sample_prompts is not None:
                logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}")

                text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy()

                prompts = []
                for line in args.sample_prompts:
                    line = line.strip()
                    if len(line) > 0 and line[0] != "#":
                        prompts.append(line)
                
                # preprocess prompts
                for i in range(len(prompts)):
                    prompt_dict = prompts[i]
                    if isinstance(prompt_dict, str):
                        from .library.train_util import line_to_prompt_dict

                        prompt_dict = line_to_prompt_dict(prompt_dict)
                        prompts[i] = prompt_dict
                    assert isinstance(prompt_dict, dict)

                    # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict.
                    prompt_dict["enum"] = i
                    prompt_dict.pop("subset", None)

                sample_prompts_te_outputs = {}  # key: prompt, value: text encoder outputs
                with accelerator.autocast(), torch.no_grad():
                    for prompt_dict in prompts:
                        for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]:
                            if p not in sample_prompts_te_outputs:
                                logger.info(f"cache Text Encoder outputs for prompt: {p}")
                                tokens_and_masks = flux_tokenize_strategy.tokenize(p)
                                sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens(
                                    flux_tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask
                                )
            self.sample_prompts_te_outputs = sample_prompts_te_outputs
            accelerator.wait_for_everyone()

            # now we can delete Text Encoders to free memory
            clip_l = None
            t5xxl = None
            clean_memory_on_device(accelerator.device)

        # load FLUX
        _, flux = flux_utils.load_flow_model(
            args.pretrained_model_name_or_path, weight_dtype, "cpu", args.disable_mmap_load_safetensors
        )

        if args.gradient_checkpointing:
            flux.enable_gradient_checkpointing(cpu_offload=args.cpu_offload_checkpointing)

        flux.requires_grad_(True)

        # block swap

        # backward compatibility
        if args.blocks_to_swap is None:
            blocks_to_swap = args.double_blocks_to_swap or 0
            if args.single_blocks_to_swap is not None:
                blocks_to_swap += args.single_blocks_to_swap // 2
            if blocks_to_swap > 0:
                logger.warning(
                    "double_blocks_to_swap and single_blocks_to_swap are deprecated. Use blocks_to_swap instead."
                    " / double_blocks_to_swapとsingle_blocks_to_swapは非推奨です。blocks_to_swapを使ってください。"
                )
                logger.info(
                    f"double_blocks_to_swap={args.double_blocks_to_swap} and single_blocks_to_swap={args.single_blocks_to_swap} are converted to blocks_to_swap={blocks_to_swap}."
                )
                args.blocks_to_swap = blocks_to_swap
            del blocks_to_swap

        self.is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0
        if self.is_swapping_blocks:
            # Swap blocks between CPU and GPU to reduce memory usage, in forward and backward passes.
            # This idea is based on 2kpr's great work. Thank you!
            logger.info(f"enable block swap: blocks_to_swap={args.blocks_to_swap}")
            flux.enable_block_swap(args.blocks_to_swap, accelerator.device)

        if not cache_latents:
            # load VAE here if not cached
            ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu")
            ae.requires_grad_(False)
            ae.eval()
            ae.to(accelerator.device, dtype=weight_dtype)

        training_models = []
        params_to_optimize = []
        training_models.append(flux)
        name_and_params = list(flux.named_parameters())
        # single param group for now
        params_to_optimize.append({"params": [p for _, p in name_and_params], "lr": args.learning_rate})
        param_names = [[n for n, _ in name_and_params]]

        # calculate number of trainable parameters
        n_params = 0
        for group in params_to_optimize:
            for p in group["params"]:
                n_params += p.numel()

        accelerator.print(f"number of trainable parameters: {n_params}")

        # 学習に必要なクラスを準備する
        accelerator.print("prepare optimizer, data loader etc.")

        if args.blockwise_fused_optimizers:
            # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
            # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters.
            # This balances memory usage and management complexity.

            # split params into groups. currently different learning rates are not supported
            grouped_params = []
            param_group = {}
            for group in params_to_optimize:
                named_parameters = list(flux.named_parameters())
                assert len(named_parameters) == len(group["params"]), "number of parameters does not match"
                for p, np in zip(group["params"], named_parameters):
                    # determine target layer and block index for each parameter
                    block_type = "other"  # double, single or other
                    if np[0].startswith("double_blocks"):
                        block_index = int(np[0].split(".")[1])
                        block_type = "double"
                    elif np[0].startswith("single_blocks"):
                        block_index = int(np[0].split(".")[1])
                        block_type = "single"
                    else:
                        block_index = -1

                    param_group_key = (block_type, block_index)
                    if param_group_key not in param_group:
                        param_group[param_group_key] = []
                    param_group[param_group_key].append(p)

            block_types_and_indices = []
            for param_group_key, param_group in param_group.items():
                block_types_and_indices.append(param_group_key)
                grouped_params.append({"params": param_group, "lr": args.learning_rate})

                num_params = 0
                for p in param_group:
                    num_params += p.numel()
                accelerator.print(f"block {param_group_key}: {num_params} parameters")

            # prepare optimizers for each group
            optimizers = []
            for group in grouped_params:
                _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group])
                optimizers.append(optimizer)
            optimizer = optimizers[0]  # avoid error in the following code

            logger.info(f"using {len(optimizers)} optimizers for blockwise fused optimizers")

            if train_util.is_schedulefree_optimizer(optimizers[0], args):
                raise ValueError("Schedule-free optimizer is not supported with blockwise fused optimizers")
            self.optimizer_train_fn = lambda: None  # dummy function
            self.optimizer_eval_fn = lambda: None  # dummy function
        else:
            _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
            self.optimizer_train_fn, self.optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(optimizer, args)

        # prepare dataloader
        # strategies are set here because they cannot be referenced in another process. Copy them with the dataset
        # some strategies can be None
        train_dataset_group.set_current_strategies()

        # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
        n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
        train_dataloader = torch.utils.data.DataLoader(
            train_dataset_group,
            batch_size=1,
            shuffle=True,
            collate_fn=collator,
            num_workers=n_workers,
            persistent_workers=args.persistent_data_loader_workers,
        )

        # 学習ステップ数を計算する
        if args.max_train_epochs is not None:
            args.max_train_steps = args.max_train_epochs * math.ceil(
                len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
            )
            accelerator.print(
                f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}"
            )

        # データセット側にも学習ステップを送信
        train_dataset_group.set_max_train_steps(args.max_train_steps)

        # lr schedulerを用意する
        if args.blockwise_fused_optimizers:
            # prepare lr schedulers for each optimizer
            lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers]
            lr_scheduler = lr_schedulers[0]  # avoid error in the following code
        else:
            lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)

        # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする
        if args.full_fp16:
            assert (
                args.mixed_precision == "fp16"
            ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
            accelerator.print("enable full fp16 training.")
            flux.to(weight_dtype)
            if clip_l is not None:
                clip_l.to(weight_dtype)
                t5xxl.to(weight_dtype)  # TODO check works with fp16 or not
        elif args.full_bf16:
            assert (
                args.mixed_precision == "bf16"
            ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。"
            accelerator.print("enable full bf16 training.")
            flux.to(weight_dtype)
            if clip_l is not None:
                clip_l.to(weight_dtype)
                t5xxl.to(weight_dtype)

        # if we don't cache text encoder outputs, move them to device
        if not args.cache_text_encoder_outputs:
            clip_l.to(accelerator.device)
            t5xxl.to(accelerator.device)

        clean_memory_on_device(accelerator.device)

        if args.deepspeed:
            ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=flux)
            # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007
            ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
                ds_model, optimizer, train_dataloader, lr_scheduler
            )
            training_models = [ds_model]

        else:
            # accelerator does some magic
            # if we doesn't swap blocks, we can move the model to device
            flux = accelerator.prepare(flux, device_placement=[not self.is_swapping_blocks])
            if self.is_swapping_blocks:
                accelerator.unwrap_model(flux).move_to_device_except_swap_blocks(accelerator.device)  # reduce peak memory usage
            optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)

        # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
        if args.full_fp16:
            # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
            # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
            train_util.patch_accelerator_for_fp16_training(accelerator)

        # resumeする
        train_util.resume_from_local_or_hf_if_specified(accelerator, args)

        if args.fused_backward_pass:
            # use fused optimizer for backward pass: other optimizers will be supported in the future
            from .library import adafactor_fused

            adafactor_fused.patch_adafactor_fused(optimizer)

            for param_group, param_name_group in zip(optimizer.param_groups, param_names):
                for parameter, param_name in zip(param_group["params"], param_name_group):
                    if parameter.requires_grad:

                        def create_grad_hook(p_name, p_group):
                            def grad_hook(tensor: torch.Tensor):
                                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                                    accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
                                optimizer.step_param(tensor, p_group)
                                tensor.grad = None

                            return grad_hook

                        parameter.register_post_accumulate_grad_hook(create_grad_hook(param_name, param_group))

        elif args.blockwise_fused_optimizers:
            # prepare for additional optimizers and lr schedulers
            for i in range(1, len(optimizers)):
                optimizers[i] = accelerator.prepare(optimizers[i])
                lr_schedulers[i] = accelerator.prepare(lr_schedulers[i])

            # counters are used to determine when to step the optimizer
            global optimizer_hooked_count
            global num_parameters_per_group
            global parameter_optimizer_map

            optimizer_hooked_count = {}
            num_parameters_per_group = [0] * len(optimizers)
            parameter_optimizer_map = {}

            for opt_idx, optimizer in enumerate(optimizers):
                for param_group in optimizer.param_groups:
                    for parameter in param_group["params"]:
                        if parameter.requires_grad:

                            def grad_hook(parameter: torch.Tensor):
                                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                                    accelerator.clip_grad_norm_(parameter, args.max_grad_norm)

                                i = parameter_optimizer_map[parameter]
                                optimizer_hooked_count[i] += 1
                                if optimizer_hooked_count[i] == num_parameters_per_group[i]:
                                    optimizers[i].step()
                                    optimizers[i].zero_grad(set_to_none=True)

                            parameter.register_post_accumulate_grad_hook(grad_hook)
                            parameter_optimizer_map[parameter] = opt_idx
                            num_parameters_per_group[opt_idx] += 1

        # epoch数を計算する
        num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
        num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
        if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
            args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1

        # 学習する
        # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
        accelerator.print("running training")
        accelerator.print(f"  num examples: {train_dataset_group.num_train_images}")
        accelerator.print(f"  num batches per epoch: {len(train_dataloader)}")
        accelerator.print(f"  num epochs: {num_train_epochs}")
        accelerator.print(
            f"  batch size per device: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
        )
        # accelerator.print(
        #     f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
        # )
        accelerator.print(f"  gradient accumulation steps = {args.gradient_accumulation_steps}")
        accelerator.print(f"  total optimization steps: {args.max_train_steps}")

        progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
        self.global_step = 0

        noise_scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift)
        noise_scheduler_copy = copy.deepcopy(noise_scheduler)

        if accelerator.is_main_process:
            init_kwargs = {}
            if args.wandb_run_name:
                init_kwargs["wandb"] = {"name": args.wandb_run_name}
            if args.log_tracker_config is not None:
                init_kwargs = toml.load(args.log_tracker_config)
            accelerator.init_trackers(
                "finetuning" if args.log_tracker_name is None else args.log_tracker_name,
                config=train_util.get_sanitized_config_or_none(args),
                init_kwargs=init_kwargs,
            )

        if self.is_swapping_blocks:
            accelerator.unwrap_model(flux).prepare_block_swap_before_forward()

        # For --sample_at_first
        #flux_train_utils.sample_images(accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs)

        self.loss_recorder = train_util.LossRecorder()
        epoch = 0  # avoid error when max_train_steps is 0

        self.tokens_and_masks = tokens_and_masks
        self.num_train_epochs = num_train_epochs
        self.current_epoch = current_epoch
        self.args = args
        self.accelerator = accelerator
        self.unet = flux
        self.vae = ae
        self.text_encoder = [clip_l, t5xxl]
        self.save_dtype = save_dtype
            
        def training_loop(break_at_steps, epoch):
            global optimizer_hooked_count
            steps_done = 0
            #accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
            progress_bar.set_description(f"Epoch {epoch + 1}/{num_train_epochs} - steps")
            current_epoch.value = epoch + 1

            for m in training_models:
                m.train()

            for step, batch in enumerate(train_dataloader):
                current_step.value = self.global_step

                if args.blockwise_fused_optimizers:
                    optimizer_hooked_count = {i: 0 for i in range(len(optimizers))}  # reset counter for each step

                with accelerator.accumulate(*training_models):
                    if "latents" in batch and batch["latents"] is not None:
                        latents = batch["latents"].to(accelerator.device, dtype=weight_dtype)
                    else:
                        with torch.no_grad():
                            # encode images to latents. images are [-1, 1]
                            latents = ae.encode(batch["images"].to(ae.dtype)).to(accelerator.device, dtype=weight_dtype)

                        # NaNが含まれていれば警告を表示し0に置き換える
                        if torch.any(torch.isnan(latents)):
                            accelerator.print("NaN found in latents, replacing with zeros")
                            latents = torch.nan_to_num(latents, 0, out=latents)

                    text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None)
                    if text_encoder_outputs_list is not None:
                        text_encoder_conds = text_encoder_outputs_list
                    else:
                        # not cached or training, so get from text encoders
                        tokens_and_masks = batch["input_ids_list"]
                        with torch.no_grad():
                            input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]]
                            text_encoder_conds = text_encoding_strategy.encode_tokens(
                                flux_tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask
                            )
                            if args.full_fp16:
                                text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds]

                    # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps

                    # Sample noise that we'll add to the latents
                    noise = torch.randn_like(latents)
                    bsz = latents.shape[0]

                    # get noisy model input and timesteps
                    noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps(
                        args, noise_scheduler_copy, latents, noise, accelerator.device, weight_dtype
                    )

                    # pack latents and get img_ids
                    packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input)  # b, c, h*2, w*2 -> b, h*w, c*4
                    packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2
                    img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device)

                    # get guidance: ensure args.guidance_scale is float
                    guidance_vec = torch.full((bsz,), float(args.guidance_scale), device=accelerator.device)

                    # call model
                    l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds
                    if not args.apply_t5_attn_mask:
                        t5_attn_mask = None

                    with accelerator.autocast():
                        # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
                        model_pred = flux(
                            img=packed_noisy_model_input,
                            img_ids=img_ids,
                            txt=t5_out,
                            txt_ids=txt_ids,
                            y=l_pooled,
                            timesteps=timesteps / 1000,
                            guidance=guidance_vec,
                            txt_attention_mask=t5_attn_mask,
                        )

                    # unpack latents
                    model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width)

                    # apply model prediction type
                    model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas)

                    # flow matching loss: this is different from SD3
                    target = noise - latents

                    # calculate loss
                    huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
                    loss = train_util.conditional_loss(model_pred.float(), target.float(), args.loss_type, "none", huber_c)
                    if weighting is not None:
                        loss = loss * weighting
                    if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
                        loss = apply_masked_loss(loss, batch)
                    loss = loss.mean([1, 2, 3])

                    loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                    loss = loss * loss_weights
                    loss = loss.mean()

                    # backward
                    accelerator.backward(loss)

                    if not (args.fused_backward_pass or args.blockwise_fused_optimizers):
                        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                            params_to_clip = []
                            for m in training_models:
                                params_to_clip.extend(m.parameters())
                            accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)

                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad(set_to_none=True)
                    else:
                        # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
                        lr_scheduler.step()
                        if args.blockwise_fused_optimizers:
                            for i in range(1, len(optimizers)):
                                lr_schedulers[i].step()

                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients:
                    progress_bar.update(1)
                    self.global_step += 1
                 

                current_loss = loss.detach().item()  # 平均なのでbatch sizeは関係ないはず
                if len(accelerator.trackers) > 0:
                    logs = {"loss": current_loss}
                    train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True)

                    accelerator.log(logs, step=self.global_step)

                self.loss_recorder.add(epoch=epoch, step=step, loss=current_loss, global_step=self.global_step)
                avr_loss: float = self.loss_recorder.moving_average
                logs = {"avr_loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
                progress_bar.set_postfix(**logs)

                if self.global_step >= break_at_steps:
                    break
                steps_done += 1

            if len(accelerator.trackers) > 0:
                logs = {"loss/epoch": self.loss_recorder.moving_average}
                accelerator.log(logs, step=epoch + 1)
            return steps_done

        return training_loop
    
def setup_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()

    add_logging_arguments(parser)
    train_util.add_sd_models_arguments(parser)  # TODO split this
    train_util.add_dataset_arguments(parser, True, True, True)
    train_util.add_training_arguments(parser, False)
    train_util.add_masked_loss_arguments(parser)
    deepspeed_utils.add_deepspeed_arguments(parser)
    train_util.add_sd_saving_arguments(parser)
    train_util.add_optimizer_arguments(parser)
    config_util.add_config_arguments(parser)
    add_custom_train_arguments(parser)  # TODO remove this from here
    train_util.add_dit_training_arguments(parser)
    flux_train_utils.add_flux_train_arguments(parser)

    parser.add_argument(
        "--mem_eff_save",
        action="store_true",
        help="[EXPERIMENTAL] use memory efficient custom model saving method / メモリ効率の良い独自のモデル保存方法を使う",
    )

    parser.add_argument(
        "--fused_optimizer_groups",
        type=int,
        default=None,
        help="**this option is not working** will be removed in the future / このオプションは動作しません。将来削除されます",
    )
    parser.add_argument(
        "--blockwise_fused_optimizers",
        action="store_true",
        help="enable blockwise optimizers for fused backward pass and optimizer step / fused backward passとoptimizer step のためブロック単位のoptimizerを有効にする",
    )
    parser.add_argument(
        "--skip_latents_validity_check",
        action="store_true",
        help="skip latents validity check / latentsの正当性チェックをスキップする",
    )
    
    parser.add_argument(
        "--cpu_offload_checkpointing",
        action="store_true",
        help="[EXPERIMENTAL] enable offloading of tensors to CPU during checkpointing / チェックポイント時にテンソルをCPUにオフロードする",
    )
    return parser
